AI Yield Farming Signal Bots: Automated Entry and Exit Strategies

pip install web3 pandas numpy scikit-learn requests asyncio

import asyncio
import pandas as pd
import numpy as np
from web3 import Web3
from sklearn.ensemble import RandomForestRegressor
import json
import time

class YieldFarmingSignalBot:
    def __init__(self, rpc_url, private_key):
        self.w3 = Web3(Web3.HTTPProvider(rpc_url))
        self.private_key = private_key
        self.account = self.w3.eth.account.from_key(private_key)
        self.signal_threshold = 0.7  # Signal confidence threshold
        self.max_position_size = 0.1  # 10% of portfolio per position
        
    async def initialize_bot(self):
        """Initialize bot with historical data and ML models"""
        print("Initializing AI yield farming signal bot...")
        await self.load_historical_data()
        await self.train_signal_model()
        print("Bot initialization complete")

    async def generate_signals(self, pool_address):
        """Generate buy/sell signals for specific yield farming pool"""
        # Fetch current pool metrics
        pool_data = await self.fetch_pool_metrics(pool_address)
        
        # Calculate technical indicators
        features = self.calculate_features(pool_data)
        
        # Generate ML prediction
        signal_strength = self.predict_yield_probability(features)
        
        # Apply risk filters
        risk_score = await self.assess_pool_risk(pool_address)
        
        # Generate final signal
        if signal_strength > self.signal_threshold and risk_score < 0.3:
            return {
                'action': 'BUY',
                'confidence': signal_strength,
                'risk_score': risk_score,
                'recommended_size': self.calculate_position_size(signal_strength),
                'timestamp': time.time()
            }
        elif signal_strength < 0.3:
            return {
                'action': 'SELL',
                'confidence': 1 - signal_strength,
                'timestamp': time.time()
            }
        
        return {'action': 'HOLD', 'timestamp': time.time()}

    def calculate_features(self, pool_data):
        """Extract features for ML model prediction"""
        features = {}
        
        # Price volatility indicators
        features['price_volatility'] = np.std(pool_data['price_history'])
        features['volume_trend'] = self.calculate_volume_trend(pool_data['volume'])
        
        # Liquidity metrics
        features['tvl_change'] = pool_data['current_tvl'] / pool_data['historical_tvl'] - 1
        features['fee_apr'] = pool_data['fees_24h'] * 365 / pool_data['current_tvl']
        
        # Market sentiment indicators
        features['social_sentiment'] = self.get_social_sentiment(pool_data['token_symbol'])
        
        return np.array(list(features.values())).reshape(1, -1)

    async def execute_entry_strategy(self, signal_data, pool_address):
        """Execute automated entry into yield farming position"""
        if signal_data['action'] != 'BUY':
            return False
            
        try:
            # Calculate optimal entry amount
            entry_amount = self.calculate_entry_amount(signal_data)
            
            # Build transaction for pool entry
            transaction = await self.build_entry_transaction(
                pool_address, 
                entry_amount
            )
            
            # Execute transaction with gas optimization
            tx_hash = await self.execute_transaction(transaction)
            
            # Record position in portfolio tracker
            await self.record_position(pool_address, entry_amount, tx_hash)
            
            print(f"Entry executed: {tx_hash}")
            return True
            
        except Exception as e:
            print(f"Entry execution failed: {str(e)}")
            return False

    async def build_entry_transaction(self, pool_address, amount):
        """Build optimized transaction for pool entry"""
        # Get pool contract interface
        pool_contract = self.get_pool_contract(pool_address)
        
        # Calculate optimal gas price
        gas_price = await self.calculate_optimal_gas()
        
        # Build transaction parameters
        transaction = pool_contract.functions.addLiquidity(
            amount,
            0,  # min_amount_out (calculated separately)
            self.account.address,
            int(time.time()) + 300  # 5 minute deadline
        ).buildTransaction({
            'from': self.account.address,
            'gas': 200000,
            'gasPrice': gas_price,
            'nonce': self.w3.eth.get_transaction_count(self.account.address)
        })
        
        return transaction

    async def monitor_exit_conditions(self):
        """Continuously monitor positions for exit signals"""
        while True:
            try:
                active_positions = await self.get_active_positions()
                
                for position in active_positions:
                    exit_signal = await self.evaluate_exit_conditions(position)
                    
                    if exit_signal['should_exit']:
                        await self.execute_exit_strategy(position, exit_signal)
                
                await asyncio.sleep(60)  # Check every minute
                
            except Exception as e:
                print(f"Exit monitoring error: {str(e)}")
                await asyncio.sleep(60)

    async def evaluate_exit_conditions(self, position):
        """Evaluate multiple exit conditions for position"""
        conditions = {}
        
        # Profit target reached
        current_value = await self.get_position_value(position)
        profit_pct = (current_value - position['entry_value']) / position['entry_value']
        conditions['profit_target'] = profit_pct > 0.15  # 15% profit target
        
        # Stop loss triggered
        conditions['stop_loss'] = profit_pct < -0.05  # 5% stop loss
        
        # APY dropped significantly
        current_apy = await self.get_current_apy(position['pool_address'])
        conditions['apy_drop'] = current_apy < position['entry_apy'] * 0.5
        
        # Smart contract risk increased
        risk_score = await self.assess_pool_risk(position['pool_address'])
        conditions['risk_increase'] = risk_score > 0.7
        
        # Time-based exit (max 30 days per position)
        position_age = time.time() - position['entry_timestamp']
        conditions['time_exit'] = position_age > 30 * 24 * 3600
        
        should_exit = any(conditions.values())
        exit_reason = [k for k, v in conditions.items() if v]
        
        return {
            'should_exit': should_exit,
            'exit_reasons': exit_reason,
            'urgency': 'HIGH' if conditions['stop_loss'] or conditions['risk_increase'] else 'NORMAL'
        }

    async def assess_pool_risk(self, pool_address):
        """Comprehensive smart contract risk assessment"""
        risk_factors = {}
        
        # Audit status check
        audit_data = await self.check_audit_status(pool_address)
        risk_factors['audit_risk'] = 0.3 if not audit_data['audited'] else 0.1
        
        # Team reputation analysis
        team_score = await self.analyze_team_reputation(pool_address)
        risk_factors['team_risk'] = 1 - team_score
        
        # Liquidity concentration risk
        liquidity_data = await self.analyze_liquidity_distribution(pool_address)
        risk_factors['liquidity_risk'] = liquidity_data['concentration_ratio']
        
        # Smart contract complexity
        contract_code = await self.get_contract_code(pool_address)
        complexity_score = self.analyze_code_complexity(contract_code)
        risk_factors['complexity_risk'] = complexity_score
        
        # Calculate composite risk score
        weights = {'audit_risk': 0.3, 'team_risk': 0.2, 'liquidity_risk': 0.3, 'complexity_risk': 0.2}
        total_risk = sum(risk_factors[factor] * weights[factor] for factor in risk_factors)
        
        return min(total_risk, 1.0)  # Cap at 100% risk

    def calculate_optimal_allocation(self, new_signal):
        """Calculate optimal position size based on portfolio diversification"""
        current_portfolio = self.get_portfolio_positions()
        
        # Protocol diversification
        protocol_exposure = self.calculate_protocol_exposure(current_portfolio)
        max_protocol_allocation = 0.25  # Max 25% per protocol
        
        # Token diversification
        token_exposure = self.calculate_token_exposure(current_portfolio)
        max_token_allocation = 0.20  # Max 20% per token
        
        # Risk-adjusted sizing
        risk_score = new_signal['risk_score']
        base_size = self.max_position_size * (1 - risk_score)
        
        # Apply diversification constraints
        protocol_limit = max_protocol_allocation - protocol_exposure.get(new_signal['protocol'], 0)
        token_limit = max_token_allocation - token_exposure.get(new_signal['token'], 0)
        
        optimal_size = min(base_size, protocol_limit, token_limit)
        
        return max(optimal_size, 0)  # Never go negative

# Launch EC2 instance with Docker
sudo yum update -y
sudo yum install docker -y
sudo service docker start
sudo usermod -a -G docker ec2-user

# Create bot deployment structure
mkdir yield-farming-bot
cd yield-farming-bot

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

CMD ["python", "main.py"]

class PerformanceTracker:
    def __init__(self):
        self.metrics = {
            'total_trades': 0,
            'successful_trades': 0,
            'total_profit': 0,
            'max_drawdown': 0,
            'sharpe_ratio': 0
        }
    
    def record_trade_outcome(self, trade_data):
        """Record individual trade results"""
        self.metrics['total_trades'] += 1
        
        if trade_data['profit'] > 0:
            self.metrics['successful_trades'] += 1
        
        self.metrics['total_profit'] += trade_data['profit']
        
        # Update drawdown tracking
        self.update_drawdown_metrics(trade_data)
        
        # Calculate updated Sharpe ratio
        self.calculate_sharpe_ratio()
    
    def generate_performance_report(self):
        """Generate comprehensive performance analysis"""
        win_rate = self.metrics['successful_trades'] / max(self.metrics['total_trades'], 1)
        
        report = {
            'win_rate': win_rate,
            'total_return': self.metrics['total_profit'],
            'max_drawdown': self.metrics['max_drawdown'],
            'sharpe_ratio': self.metrics['sharpe_ratio'],
            'total_trades': self.metrics['total_trades']
        }
        
        return report

async def calculate_optimal_gas(self):
    """Calculate optimal gas price based on network conditions"""
    # Get current network gas prices
    gas_station_data = await self.fetch_gas_station_api()
    
    # Factor in transaction urgency
    if self.current_signal_urgency == 'HIGH':
        gas_multiplier = 1.5  # Pay premium for urgent transactions
    else:
        gas_multiplier = 1.0
    
    # Calculate optimal price
    optimal_gas = int(gas_station_data['standard'] * gas_multiplier)
    
    # Apply maximum gas limit
    max_gas = 100  # Never pay more than 100 gwei
    
    return min(optimal_gas, max_gas)

async def execute_mev_protected_transaction(self, transaction):
    """Execute transaction with MEV protection"""
    # Use Flashbots or similar service
    flashbots_bundle = [
        {
            'transaction': transaction,
            'signer': self.account
        }
    ]
    
    # Submit bundle to avoid public mempool
    bundle_hash = await self.submit_flashbots_bundle(flashbots_bundle)
    
    return bundle_hash

async def handle_protocol_changes(self, pool_address):
    """Adapt to protocol updates and changes"""
    try:
        # Detect contract upgrades
        current_implementation = await self.get_implementation_address(pool_address)
        
        if current_implementation != self.known_implementations[pool_address]:
            # Protocol upgraded - update interface
            await self.update_contract_interface(pool_address, current_implementation)
            
        # Test contract functionality
        test_result = await self.test_contract_methods(pool_address)
        
        if not test_result['success']:
            # Disable trading for this pool until manual review
            await self.disable_pool_trading(pool_address)
            
    except Exception as e:
        print(f"Protocol change handling error: {str(e)}")
        await self.disable_pool_trading(pool_address)

class MultiChainYieldBot:
    def __init__(self):
        self.chains = {
            'ethereum': {'rpc': 'https://eth-mainnet.alchemyapi.io/v2/your-key', 'chain_id': 1},
            'polygon': {'rpc': 'https://polygon-mainnet.alchemyapi.io/v2/your-key', 'chain_id': 137},
            'arbitrum': {'rpc': 'https://arb-mainnet.g.alchemy.com/v2/your-key', 'chain_id': 42161},
            'avalanche': {'rpc': 'https://api.avax.network/ext/bc/C/rpc', 'chain_id': 43114}
        }
        
        self.chain_bots = {}
        self.initialize_chain_bots()
    
    async def scan_all_chains(self):
        """Scan opportunities across all supported chains"""
        tasks = []
        
        for chain_name, bot in self.chain_bots.items():
            task = asyncio.create_task(bot.scan_opportunities())
            tasks.append(task)
        
        # Execute scans in parallel
        results = await asyncio.gather(*tasks)
        
        # Consolidate and rank opportunities
        all_opportunities = []
        for chain_result in results:
            all_opportunities.extend(chain_result)
        
        # Sort by risk-adjusted returns
        ranked_opportunities = sorted(
            all_opportunities, 
            key=lambda x: x['expected_return'] / (1 + x['risk_score']), 
            reverse=True
        )
        
        return ranked_opportunities[:10]  # Return top 10 opportunities

class SignalMarketplace:
    def __init__(self, bot_instance):
        self.bot = bot_instance
        self.subscribers = {}
        self.signal_history = []
    
    async def broadcast_signal(self, signal_data):
        """Broadcast signals to paying subscribers"""
        # Add performance metrics to signal
        enhanced_signal = await self.enhance_signal_data(signal_data)
        
        # Broadcast to different tiers
        for subscriber_id, subscription in self.subscribers.items():
            if subscription['tier'] == 'premium':
                # Send detailed signal with reasoning
                await self.send_premium_signal(subscriber_id, enhanced_signal)
            elif subscription['tier'] == 'basic':
                # Send basic signal only
                await self.send_basic_signal(subscriber_id, enhanced_signal)
    
    async def enhance_signal_data(self, signal_data):
        """Add performance metrics and reasoning to signals"""
        enhanced = signal_data.copy()
        
        # Add bot performance context
        enhanced['bot_performance'] = await self.bot.get_performance_metrics()
        
        # Add reasoning explanation
        enhanced['reasoning'] = self.generate_signal_reasoning(signal_data)
        
        # Add confidence intervals
        enhanced['confidence_interval'] = self.calculate_confidence_bounds(signal_data)
        
        return enhanced